Abstract
We introduce a noise-tolerant segmentation algorithm efficient on 3D multiscale granular materials. The approach uses a graph-based version of the stochastic watershed and relies on the morphological granulometry of the image to achieve a content-driven unsupervised segmentation. We present results on both a virtual material and a real X-ray microtomographic image of solid propellant.
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Gillibert, L., Jeulin, D. (2011). Stochastic Multiscale Segmentation Constrained by Image Content. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds) Mathematical Morphology and Its Applications to Image and Signal Processing. ISMM 2011. Lecture Notes in Computer Science, vol 6671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21569-8_12
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DOI: https://doi.org/10.1007/978-3-642-21569-8_12
Publisher Name: Springer, Berlin, Heidelberg
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